CSV (Comma-Separated Values) /vs/ Parquet (Columnar Storage Format)

Parquet  and CSV are two common file formats used for storing and processing data. Each has its own strengths and weaknesses, and the choice between them depends on your specific use case. Below is a detailed comparison of Parquet and CSV:

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1. Overview

Feature	CSV (Comma-Separated Values)	Parquet (Columnar Storage Format)
Format	Row-based (stores data row by row)	Columnar (stores data column by column)
Compression	Limited or no compression	Highly compressed (e.g., Snappy, GZIP, ZSTD)
Schema	Schema-less (no metadata about data types)	Schema-aware (stores metadata about data types)
Read Performance	Slower for large datasets (reads entire rows)	Faster for large datasets (reads only needed columns)
Write Performance	Faster for small datasets	Slower for small datasets (due to compression and columnar storage)
Storage Efficiency	Less efficient (stores data as plain text)	Highly efficient (compressed and columnar storage)
Use Case	Simple data exchange, small datasets, human-readable	Big data processing, analytics, large datasets

 

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2. Key Differences

 

	CSV (Comma-Separated Values)	Parquet (Columnar Storage Format)
Storage Format	Row-based storage: Each row is stored as a line of text, with values separated by commas (or other delimiters). Human-readable: Easy to view and edit using text editors or spreadsheet software. No metadata: Does not store information about data types or schema.	Columnar storage: Data is stored column by column, which is more efficient for analytical queries. Binary format: Not human-readable, optimized for machine processing. Schema-aware: Stores metadata about data types, making it self-describing.
Compression	Typically uncompressed or uses basic compression (e.g., GZIP). Larger file sizes compared to Parquet.	Highly compressed: Uses advanced compression algorithms (e.g., Snappy, GZIP, ZSTD). Smaller file sizes, reducing storage costs and improving I/O performance.
Performance	Slower for analytical queries: Reads entire rows, even if only a few columns are needed. Suitable for small datasets or simple data exchange.	Faster for analytical queries: Reads only the required columns, reducing I/O. Optimized for big data processing and analytics.
Schema Evolution	Schema-less: Changes in schema (e.g., adding/removing columns) require manual handling. No support for complex data types (e.g., nested structures).	Schema-aware: Supports schema evolution (e.g., adding/removing columns without rewriting the entire dataset). Supports complex data types (e.g., arrays, maps, nested structures).
Use Cases	Simple data exchange between systems. Small datasets or prototyping. Human-readable format for manual inspection.	Big data processing and analytics. Large datasets with complex schemas. Efficient storage and querying in distributed systems (e.g., Hadoop, Spark).

 

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3. Pros and Cons

CSV

Pros	Cons
Simple and human-readable	No compression (large file sizes)
Easy to create and edit	No schema or metadata support
Supported by almost all tools and systems	Slow for large datasets and complex queries
Suitable for small datasets	Limited support for complex data types

 

Parquet

Pros	Cons
Highly compressed (small file sizes)	Not human-readable
Columnar storage (fast for analytics)	Slower to write (due to compression)
Schema-aware (supports complex data types)	Requires tools/libraries to read/write
Optimized for big data processing	Overhead for small datasets

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4. When to Use CSV

• Small datasets : When working with small datasets that don't require advanced compression or performance optimizations.
• Human-readable format : When you need to manually inspect or edit the data.
• Simple data exchange : When exchanging data with systems that only support CSV.
• Prototyping : When quickly prototyping or testing data pipelines.

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5. When to Use Parquet

• Big data processing : When working with large datasets in distributed systems (e.g., Hadoop, Spark).
• Analytical queries : When performing analytical queries that require reading specific columns.
• Storage efficiency : When you need to reduce storage costs and improve I/O performance.
• Complex data types : When working with nested or complex data structures.

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6. Example Use Cases

CSV

• Exporting data from a database for manual analysis.
• Sharing small datasets with non-technical users.
• Loading data into a spreadsheet or simple database.

Parquet

• Storing large datasets in a data lake or data warehouse.
• Running analytical queries on big data platforms (e.g., Spark, Hive).
• Optimizing storage and query performance in distributed systems.

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7. Example Code

Reading and Writing CSV in PySpark

Spark Sample Code (CSV)

from pyspark.sql import SparkSession   # Initialize Spark session spark = SparkSession.builder.appName("CSV Example").getOrCreate()   # Read CSV df_csv = spark.read.csv("data.csv", header=True, inferSchema=True)   # Write CSV df_csv.write.csv("output.csv", header=True)

 

 

 

 

 

 

 

 

Reading and Writing Parquet in PySpark

Spark Sample Code (Parquet)

from pyspark.sql import SparkSession   # Initialize Spark session spark = SparkSession.builder.appName("Parquet Example").getOrCreate()   # Read Parquet df_parquet = spark.read.parquet("data.parquet")   # Write Parquet df_parquet.write.parquet("output.parquet")

 

 

 

 

 

 

 

 

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8. Summary

Feature	CSV	Parquet
Best For	Small datasets, human-readable format	Big data, analytics, storage efficiency
Compression	Limited or none	Highly compressed
Schema	Schema-less	Schema-aware
Performance	Slower for large datasets	Faster for analytical queries
Complex Data Types	Not supported	Supported

 

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Conclusion

• Use CSV for small datasets, simple data exchange, or when human readability is important.
• Use Parquet for big data processing, analytical queries, and efficient storage in distributed systems.